We present INSPIRE, a top-performing general-purpose method for deformable image registration. INSPIRE brings distance measures which combine intensity and spatial information into an elastic B-splines-based transformation model and incorporates an inverse inconsistency penalization supporting symmetric registration performance. We introduce several theoretical and algorithmic solutions which provide high computational efficiency and thereby applicability of the proposed framework in a wide range of real scenarios. We show that INSPIRE delivers highly accurate, as well as stable and robust registration results. We evaluate the method on a 2D dataset created from retinal images, characterized by presence of networks of thin structures. Here INSPIRE exhibits excellent performance, substantially outperforming the widely used reference methods. {We also evaluate INSPIRE on the Fundus Image Registration Dataset (FIRE), which consists of 134 pairs of separately acquired retinal images. INSPIRE exhibits excellent performance on the FIRE dataset, substantially outperforming several domain-specific methods.} We also evaluate the method on four benchmark datasets of 3D magnetic resonance images of brains, for a total of 2088 pairwise registrations. A comparison with 17 other state-of-the-art methods reveals that INSPIRE provides the best overall performance. Code is available at http://github.com/MIDA-group/inspire

翻译：我们提出INSPIRE，一种面向可变形图像配准的通用高性能方法。INSPIRE将融合强度与空间信息的距离度量引入基于弹性B样条的变换模型，并采用逆不一致性惩罚机制以实现对称配准性能。我们引入了多项理论与算法优化方案，显著提升了计算效率，从而使该框架能够广泛应用于各类实际场景。实验表明，INSPIRE能够提供高精度、稳定且鲁棒的配准结果。我们首先在视网膜图像构建的二维数据集上评估该方法，该数据集以薄结构网络的存在为特征。在此场景下，INSPIRE展现出卓越性能，大幅优于广泛使用的参考方法。此外，我们在眼底图像配准数据集（FIRE）上进行评估，该数据集包含134组独立采集的视网膜图像对。INSPIRE在FIRE数据集上同样表现优异，显著超越多种领域专用方法。我们还利用四个基准数据集对方法进行评估，涉及大脑三维磁共振图像，共计2088对配准任务。与17种其他前沿方法的比较表明，INSPIRE提供了最佳的整体性能。相关代码已开源至http://github.com/MIDA-group/inspire。